Overview

Dataset statistics

Number of variables35
Number of observations133598
Missing cells118
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.8 MiB
Average record size in memory273.0 B

Variable types

Categorical13
Numeric21
Boolean1

Alerts

name has a high cardinality: 3158 distinct values High cardinality
kickoff_time has a high cardinality: 1384 distinct values High cardinality
bonus is highly correlated with goals_scoredHigh correlation
bps is highly correlated with creativity and 7 other fieldsHigh correlation
clean_sheets is highly correlated with total_pointsHigh correlation
creativity is highly correlated with bps and 7 other fieldsHigh correlation
fixture is highly correlated with round and 1 other fieldsHigh correlation
goals_conceded is highly correlated with bps and 5 other fieldsHigh correlation
goals_scored is highly correlated with bonusHigh correlation
ict_index is highly correlated with bps and 7 other fieldsHigh correlation
influence is highly correlated with bps and 7 other fieldsHigh correlation
minutes is highly correlated with bps and 8 other fieldsHigh correlation
round is highly correlated with fixture and 1 other fieldsHigh correlation
selected is highly correlated with minutes and 2 other fieldsHigh correlation
threat is highly correlated with bps and 5 other fieldsHigh correlation
total_points is highly correlated with bps and 8 other fieldsHigh correlation
transfers_in is highly correlated with bps and 7 other fieldsHigh correlation
transfers_out is highly correlated with selected and 1 other fieldsHigh correlation
GW is highly correlated with fixture and 1 other fieldsHigh correlation
bonus is highly correlated with bps and 4 other fieldsHigh correlation
bps is highly correlated with bonus and 7 other fieldsHigh correlation
clean_sheets is highly correlated with bps and 1 other fieldsHigh correlation
creativity is highly correlated with bps and 3 other fieldsHigh correlation
fixture is highly correlated with round and 1 other fieldsHigh correlation
goals_conceded is highly correlated with minutesHigh correlation
goals_scored is highly correlated with bonus and 5 other fieldsHigh correlation
ict_index is highly correlated with bonus and 7 other fieldsHigh correlation
influence is highly correlated with bonus and 7 other fieldsHigh correlation
minutes is highly correlated with bps and 5 other fieldsHigh correlation
round is highly correlated with fixture and 1 other fieldsHigh correlation
selected is highly correlated with transfers_in and 1 other fieldsHigh correlation
threat is highly correlated with goals_scored and 3 other fieldsHigh correlation
total_points is highly correlated with bonus and 7 other fieldsHigh correlation
transfers_balance is highly correlated with transfers_in and 1 other fieldsHigh correlation
transfers_in is highly correlated with selected and 1 other fieldsHigh correlation
transfers_out is highly correlated with selected and 1 other fieldsHigh correlation
GW is highly correlated with fixture and 1 other fieldsHigh correlation
bonus is highly correlated with goals_scoredHigh correlation
bps is highly correlated with creativity and 6 other fieldsHigh correlation
creativity is highly correlated with bps and 6 other fieldsHigh correlation
fixture is highly correlated with round and 1 other fieldsHigh correlation
goals_conceded is highly correlated with bps and 4 other fieldsHigh correlation
goals_scored is highly correlated with bonusHigh correlation
ict_index is highly correlated with bps and 6 other fieldsHigh correlation
influence is highly correlated with bps and 6 other fieldsHigh correlation
minutes is highly correlated with bps and 6 other fieldsHigh correlation
round is highly correlated with fixture and 1 other fieldsHigh correlation
selected is highly correlated with transfers_in and 1 other fieldsHigh correlation
threat is highly correlated with bps and 5 other fieldsHigh correlation
total_points is highly correlated with bps and 5 other fieldsHigh correlation
transfers_in is highly correlated with selected and 1 other fieldsHigh correlation
transfers_out is highly correlated with selected and 1 other fieldsHigh correlation
GW is highly correlated with fixture and 1 other fieldsHigh correlation
assists is highly correlated with bps and 1 other fieldsHigh correlation
bonus is highly correlated with bps and 3 other fieldsHigh correlation
bps is highly correlated with assists and 9 other fieldsHigh correlation
clean_sheets is highly correlated with bps and 2 other fieldsHigh correlation
creativity is highly correlated with ict_indexHigh correlation
fixture is highly correlated with round and 1 other fieldsHigh correlation
goals_conceded is highly correlated with bps and 3 other fieldsHigh correlation
goals_scored is highly correlated with bps and 4 other fieldsHigh correlation
ict_index is highly correlated with bonus and 7 other fieldsHigh correlation
influence is highly correlated with bonus and 6 other fieldsHigh correlation
minutes is highly correlated with bps and 5 other fieldsHigh correlation
round is highly correlated with fixture and 1 other fieldsHigh correlation
selected is highly correlated with transfers_balance and 1 other fieldsHigh correlation
team_h_score is highly correlated with goals_concededHigh correlation
threat is highly correlated with bps and 4 other fieldsHigh correlation
total_points is highly correlated with assists and 9 other fieldsHigh correlation
transfers_balance is highly correlated with selected and 2 other fieldsHigh correlation
transfers_in is highly correlated with transfers_balanceHigh correlation
transfers_out is highly correlated with transfers_balanceHigh correlation
value is highly correlated with selectedHigh correlation
GW is highly correlated with fixture and 1 other fieldsHigh correlation
bps has 74250 (55.6%) zeros Zeros
creativity has 80158 (60.0%) zeros Zeros
goals_conceded has 96773 (72.4%) zeros Zeros
ict_index has 75236 (56.3%) zeros Zeros
influence has 77609 (58.1%) zeros Zeros
minutes has 73252 (54.8%) zeros Zeros
saves has 129543 (97.0%) zeros Zeros
team_a_score has 43513 (32.6%) zeros Zeros
team_h_score has 32545 (24.4%) zeros Zeros
threat has 94571 (70.8%) zeros Zeros
total_points has 76033 (56.9%) zeros Zeros
transfers_balance has 4824 (3.6%) zeros Zeros
transfers_in has 9059 (6.8%) zeros Zeros
transfers_out has 4217 (3.2%) zeros Zeros

Reproduction

Analysis started2022-07-16 14:56:37.477443
Analysis finished2022-07-16 14:57:52.955462
Duration1 minute and 15.48 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

name
Categorical

HIGH CARDINALITY

Distinct3158
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Ben Davies
 
113
Aaron_Cresswell
 
76
Leroy_Fer
 
76
Manuel_Agudo Durán
 
76
Mame Biram_Diouf
 
76
Other values (3153)
133181 

Length

Max length44
Median length40
Mean length15.68106558
Min length7

Characters and Unicode

Total characters2094959
Distinct characters114
Distinct categories16 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st rowAaron_Cresswell
2nd rowAaron_Lennon
3rd rowAaron_Ramsey
4th rowAbdoulaye_Doucouré
5th rowAbdul Rahman_Baba

Common Values

ValueCountFrequency (%)
Ben Davies113
 
0.1%
Aaron_Cresswell76
 
0.1%
Leroy_Fer76
 
0.1%
Manuel_Agudo Durán76
 
0.1%
Mame Biram_Diouf76
 
0.1%
Mamadou_Sakho76
 
0.1%
Maarten_Stekelenburg76
 
0.1%
Lys_Mousset76
 
0.1%
Luke_Shaw76
 
0.1%
Lukasz_Fabianski76
 
0.1%
Other values (3148)132801
99.4%

Length

2022-07-16T16:57:53.031461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de1281
 
0.6%
silva1050
 
0.5%
james903
 
0.5%
da699
 
0.4%
ben582
 
0.3%
santos498
 
0.3%
jack493
 
0.2%
ryan485
 
0.2%
gabriel447
 
0.2%
nathan385
 
0.2%
Other values (3824)191983
96.6%

Most occurring characters

ValueCountFrequency (%)
a184544
 
8.8%
e165076
 
7.9%
_134846
 
6.4%
n133022
 
6.3%
o124540
 
5.9%
r118689
 
5.7%
i117044
 
5.6%
l86783
 
4.1%
s75250
 
3.6%
65208
 
3.1%
Other values (104)889957
42.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1453000
69.4%
Uppercase Letter301832
 
14.4%
Connector Punctuation134846
 
6.4%
Decimal Number124813
 
6.0%
Space Separator65208
 
3.1%
Dash Punctuation5119
 
0.2%
Other Symbol2939
 
0.1%
Other Punctuation2794
 
0.1%
Format1144
 
0.1%
Other Number816
 
< 0.1%
Other values (6)2448
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a184544
12.7%
e165076
11.4%
n133022
 
9.2%
o124540
 
8.6%
r118689
 
8.2%
i117044
 
8.1%
l86783
 
6.0%
s75250
 
5.2%
t57466
 
4.0%
d51808
 
3.6%
Other values (35)338778
23.3%
Uppercase Letter
ValueCountFrequency (%)
M30093
 
10.0%
S23735
 
7.9%
A23578
 
7.8%
J22739
 
7.5%
C19532
 
6.5%
D18110
 
6.0%
B17197
 
5.7%
R15342
 
5.1%
L14093
 
4.7%
G11645
 
3.9%
Other values (22)105768
35.0%
Decimal Number
ValueCountFrequency (%)
116833
13.5%
216672
13.4%
316653
13.3%
416427
13.2%
514492
11.6%
69521
7.6%
78628
6.9%
88574
6.9%
98532
6.8%
08481
6.8%
Control
ValueCountFrequency (%)
‡206
28.3%
Ÿ165
22.7%
˜121
16.6%
–77
 
10.6%
68
 
9.4%
œ36
 
5.0%
‚29
 
4.0%
‘25
 
3.4%
Other Punctuation
ValueCountFrequency (%)
¡1474
52.8%
'638
22.8%
520
 
18.6%
§162
 
5.8%
Currency Symbol
ValueCountFrequency (%)
£451
77.1%
¤105
 
17.9%
¢29
 
5.0%
Other Number
ValueCountFrequency (%)
¼557
68.3%
³259
31.7%
Modifier Symbol
ValueCountFrequency (%)
¯207
51.6%
¸194
48.4%
Connector Punctuation
ValueCountFrequency (%)
_134846
100.0%
Space Separator
ValueCountFrequency (%)
65208
100.0%
Dash Punctuation
ValueCountFrequency (%)
-5119
100.0%
Other Symbol
ValueCountFrequency (%)
©2939
100.0%
Format
ValueCountFrequency (%)
­1144
100.0%
Other Letter
ValueCountFrequency (%)
º557
100.0%
Initial Punctuation
ValueCountFrequency (%)
«171
100.0%
Math Symbol
ValueCountFrequency (%)
±7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1755389
83.8%
Common339570
 
16.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a184544
 
10.5%
e165076
 
9.4%
n133022
 
7.6%
o124540
 
7.1%
r118689
 
6.8%
i117044
 
6.7%
l86783
 
4.9%
s75250
 
4.3%
t57466
 
3.3%
d51808
 
3.0%
Other values (68)641167
36.5%
Common
ValueCountFrequency (%)
_134846
39.7%
65208
19.2%
116833
 
5.0%
216672
 
4.9%
316653
 
4.9%
416427
 
4.8%
514492
 
4.3%
69521
 
2.8%
78628
 
2.5%
88574
 
2.5%
Other values (26)31716
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2067598
98.7%
None27361
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a184544
 
8.9%
e165076
 
8.0%
_134846
 
6.5%
n133022
 
6.4%
o124540
 
6.0%
r118689
 
5.7%
i117044
 
5.7%
l86783
 
4.2%
s75250
 
3.6%
65208
 
3.2%
Other values (56)862596
41.7%
None
ValueCountFrequency (%)
Ã9273
33.9%
é3322
 
12.1%
©2939
 
10.7%
¡1474
 
5.4%
­1144
 
4.2%
á1119
 
4.1%
í891
 
3.3%
ö570
 
2.1%
¼557
 
2.0%
º557
 
2.0%
Other values (38)5515
20.2%

assists
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
128734 
1
 
4468
2
 
368
3
 
26
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters133598
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0128734
96.4%
14468
 
3.3%
2368
 
0.3%
326
 
< 0.1%
42
 
< 0.1%

Length

2022-07-16T16:57:53.118463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-16T16:57:53.196464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0128734
96.4%
14468
 
3.3%
2368
 
0.3%
326
 
< 0.1%
42
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0128734
96.4%
14468
 
3.3%
2368
 
0.3%
326
 
< 0.1%
42
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number133598
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0128734
96.4%
14468
 
3.3%
2368
 
0.3%
326
 
< 0.1%
42
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common133598
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0128734
96.4%
14468
 
3.3%
2368
 
0.3%
326
 
< 0.1%
42
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII133598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0128734
96.4%
14468
 
3.3%
2368
 
0.3%
326
 
< 0.1%
42
 
< 0.1%

bonus
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
126581 
1
 
2370
3
 
2358
2
 
2289

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters133598
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0126581
94.7%
12370
 
1.8%
32358
 
1.8%
22289
 
1.7%

Length

2022-07-16T16:57:53.260962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-16T16:57:53.331964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0126581
94.7%
12370
 
1.8%
32358
 
1.8%
22289
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0126581
94.7%
12370
 
1.8%
32358
 
1.8%
22289
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number133598
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0126581
94.7%
12370
 
1.8%
32358
 
1.8%
22289
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common133598
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0126581
94.7%
12370
 
1.8%
32358
 
1.8%
22289
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII133598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0126581
94.7%
12370
 
1.8%
32358
 
1.8%
22289
 
1.7%

bps
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct112
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.028076768
Minimum-19
Maximum114
Zeros74250
Zeros (%)55.6%
Negative1693
Negative (%)1.3%
Memory size1.0 MiB
2022-07-16T16:57:53.405965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-19
5-th percentile0
Q10
median0
Q310
95-th percentile27
Maximum114
Range133
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.784385431
Coefficient of variation (CV)1.623135505
Kurtosis5.066219879
Mean6.028076768
Median Absolute Deviation (MAD)0
Skewness1.9977969
Sum805339
Variance95.73419825
MonotonicityNot monotonic
2022-07-16T16:57:53.500963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
074250
55.6%
34658
 
3.5%
42708
 
2.0%
22668
 
2.0%
122577
 
1.9%
132575
 
1.9%
112467
 
1.8%
102454
 
1.8%
142388
 
1.8%
92314
 
1.7%
Other values (102)34539
25.9%
ValueCountFrequency (%)
-191
 
< 0.1%
-181
 
< 0.1%
-161
 
< 0.1%
-151
 
< 0.1%
-143
 
< 0.1%
-124
 
< 0.1%
-116
 
< 0.1%
-1016
< 0.1%
-917
< 0.1%
-835
< 0.1%
ValueCountFrequency (%)
1141
< 0.1%
1041
< 0.1%
1021
< 0.1%
991
< 0.1%
961
< 0.1%
942
< 0.1%
932
< 0.1%
922
< 0.1%
902
< 0.1%
891
< 0.1%

clean_sheets
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
119570 
1
14028 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters133598
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0119570
89.5%
114028
 
10.5%

Length

2022-07-16T16:57:53.586464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-16T16:57:53.664463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0119570
89.5%
114028
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0119570
89.5%
114028
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number133598
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0119570
89.5%
114028
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
Common133598
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0119570
89.5%
114028
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII133598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0119570
89.5%
114028
 
10.5%

creativity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct890
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.789518556
Minimum0
Maximum170.9
Zeros80158
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-07-16T16:57:53.735463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.7
95-th percentile27.6
Maximum170.9
Range170.9
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation10.78586477
Coefficient of variation (CV)2.251972645
Kurtosis14.30772088
Mean4.789518556
Median Absolute Deviation (MAD)0
Skewness3.30414951
Sum639870.1
Variance116.3348789
MonotonicityNot monotonic
2022-07-16T16:57:53.821463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
080158
60.0%
0.32915
 
2.2%
0.11534
 
1.1%
0.81502
 
1.1%
1.31117
 
0.8%
0.51116
 
0.8%
0.2985
 
0.7%
0.4931
 
0.7%
0.6883
 
0.7%
1.8866
 
0.6%
Other values (880)41591
31.1%
ValueCountFrequency (%)
080158
60.0%
0.11534
 
1.1%
0.2985
 
0.7%
0.32915
 
2.2%
0.4931
 
0.7%
0.51116
 
0.8%
0.6883
 
0.7%
0.7749
 
0.6%
0.81502
 
1.1%
0.9636
 
0.5%
ValueCountFrequency (%)
170.91
< 0.1%
136.21
< 0.1%
134.81
< 0.1%
134.31
< 0.1%
133.51
< 0.1%
127.71
< 0.1%
123.91
< 0.1%
118.11
< 0.1%
114.81
< 0.1%
114.41
< 0.1%

element
Real number (ℝ≥0)

Distinct714
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.9853291
Minimum1
Maximum714
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-07-16T16:57:53.914464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31
Q1153
median306
Q3459
95-th percentile594
Maximum714
Range713
Interquartile range (IQR)306

Descriptive statistics

Standard deviation179.9199976
Coefficient of variation (CV)0.5841836626
Kurtosis-1.104059664
Mean307.9853291
Median Absolute Deviation (MAD)153
Skewness0.06763434893
Sum41146224
Variance32371.20553
MonotonicityNot monotonic
2022-07-16T16:57:54.001962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
489221
 
0.2%
440221
 
0.2%
490221
 
0.2%
427221
 
0.2%
443221
 
0.2%
90220
 
0.2%
189220
 
0.2%
414220
 
0.2%
438220
 
0.2%
446220
 
0.2%
Other values (704)131393
98.3%
ValueCountFrequency (%)
1219
0.2%
2219
0.2%
3219
0.2%
4219
0.2%
5219
0.2%
6219
0.2%
7219
0.2%
8219
0.2%
9219
0.2%
10220
0.2%
ValueCountFrequency (%)
7145
< 0.1%
7136
< 0.1%
7125
< 0.1%
7117
< 0.1%
7106
< 0.1%
7096
< 0.1%
7087
< 0.1%
7078
< 0.1%
7068
< 0.1%
7059
< 0.1%

fixture
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct380
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191.4343179
Minimum1
Maximum380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-07-16T16:57:54.097461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21
Q1100
median193
Q3281
95-th percentile360
Maximum380
Range379
Interquartile range (IQR)181

Descriptive statistics

Standard deviation107.3900055
Coefficient of variation (CV)0.5609757264
Kurtosis-1.153204556
Mean191.4343179
Median Absolute Deviation (MAD)91
Skewness-0.02045189867
Sum25575242
Variance11532.61329
MonotonicityNot monotonic
2022-07-16T16:57:54.185462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
275437
 
0.3%
279427
 
0.3%
300417
 
0.3%
252415
 
0.3%
259413
 
0.3%
251406
 
0.3%
291406
 
0.3%
270405
 
0.3%
289403
 
0.3%
261403
 
0.3%
Other values (370)129466
96.9%
ValueCountFrequency (%)
1310
0.2%
2317
0.2%
3310
0.2%
4311
0.2%
5309
0.2%
6320
0.2%
7325
0.2%
8327
0.2%
9328
0.2%
10316
0.2%
ValueCountFrequency (%)
380350
0.3%
379323
0.2%
378352
0.3%
377325
0.2%
376321
0.2%
375327
0.2%
374334
0.3%
373330
0.2%
372344
0.3%
371321
0.2%

goals_conceded
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4950074103
Minimum0
Maximum9
Zeros96773
Zeros (%)72.4%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-07-16T16:57:54.264962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9676928672
Coefficient of variation (CV)1.95490582
Kurtosis6.336407128
Mean0.4950074103
Median Absolute Deviation (MAD)0
Skewness2.353226786
Sum66132
Variance0.9364294853
MonotonicityNot monotonic
2022-07-16T16:57:54.321465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
096773
72.4%
118703
 
14.0%
210678
 
8.0%
34837
 
3.6%
41784
 
1.3%
5629
 
0.5%
6119
 
0.1%
750
 
< 0.1%
917
 
< 0.1%
88
 
< 0.1%
ValueCountFrequency (%)
096773
72.4%
118703
 
14.0%
210678
 
8.0%
34837
 
3.6%
41784
 
1.3%
5629
 
0.5%
6119
 
0.1%
750
 
< 0.1%
88
 
< 0.1%
917
 
< 0.1%
ValueCountFrequency (%)
917
 
< 0.1%
88
 
< 0.1%
750
 
< 0.1%
6119
 
0.1%
5629
 
0.5%
41784
 
1.3%
34837
 
3.6%
210678
 
8.0%
118703
 
14.0%
096773
72.4%

goals_scored
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
128395 
1
 
4662
2
 
477
3
 
58
4
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters133598
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0128395
96.1%
14662
 
3.5%
2477
 
0.4%
358
 
< 0.1%
46
 
< 0.1%

Length

2022-07-16T16:57:54.386462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-16T16:57:54.458964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0128395
96.1%
14662
 
3.5%
2477
 
0.4%
358
 
< 0.1%
46
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0128395
96.1%
14662
 
3.5%
2477
 
0.4%
358
 
< 0.1%
46
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number133598
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0128395
96.1%
14662
 
3.5%
2477
 
0.4%
358
 
< 0.1%
46
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common133598
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0128395
96.1%
14662
 
3.5%
2477
 
0.4%
358
 
< 0.1%
46
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII133598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0128395
96.1%
14662
 
3.5%
2477
 
0.4%
358
 
< 0.1%
46
 
< 0.1%

ict_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct274
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.73615623
Minimum0
Maximum35.8
Zeros75236
Zeros (%)56.3%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-07-16T16:57:54.534963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.5
95-th percentile8.2
Maximum35.8
Range35.8
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation3.061252245
Coefficient of variation (CV)1.763235469
Kurtosis8.663819987
Mean1.73615623
Median Absolute Deviation (MAD)0
Skewness2.595539149
Sum231947
Variance9.371265305
MonotonicityNot monotonic
2022-07-16T16:57:54.625963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
075236
56.3%
0.21229
 
0.9%
2.31179
 
0.9%
2.11179
 
0.9%
1.91175
 
0.9%
21163
 
0.9%
1.81158
 
0.9%
0.11126
 
0.8%
2.21105
 
0.8%
1.51080
 
0.8%
Other values (264)47968
35.9%
ValueCountFrequency (%)
075236
56.3%
0.11126
 
0.8%
0.21229
 
0.9%
0.31071
 
0.8%
0.4931
 
0.7%
0.5881
 
0.7%
0.6788
 
0.6%
0.7860
 
0.6%
0.8783
 
0.6%
0.9853
 
0.6%
ValueCountFrequency (%)
35.81
< 0.1%
32.81
< 0.1%
31.11
< 0.1%
30.41
< 0.1%
30.12
< 0.1%
29.51
< 0.1%
28.21
< 0.1%
28.11
< 0.1%
281
< 0.1%
27.91
< 0.1%

influence
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct534
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.213058579
Minimum0
Maximum163.6
Zeros77609
Zeros (%)58.1%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-07-16T16:57:54.722965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q310.8
95-th percentile34.8
Maximum163.6
Range163.6
Interquartile range (IQR)10.8

Descriptive statistics

Standard deviation12.87292689
Coefficient of variation (CV)1.784669672
Kurtosis8.802516867
Mean7.213058579
Median Absolute Deviation (MAD)0
Skewness2.558131224
Sum963650.2
Variance165.7122467
MonotonicityNot monotonic
2022-07-16T16:57:54.813962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
077609
58.1%
0.2912
 
0.7%
2648
 
0.5%
2.2602
 
0.5%
1576
 
0.4%
1.2554
 
0.4%
2.4509
 
0.4%
0.4508
 
0.4%
3.2506
 
0.4%
3491
 
0.4%
Other values (524)50683
37.9%
ValueCountFrequency (%)
077609
58.1%
0.2912
 
0.7%
0.4508
 
0.4%
0.6364
 
0.3%
0.8254
 
0.2%
1576
 
0.4%
1.2554
 
0.4%
1.4434
 
0.3%
1.6336
 
0.3%
1.8283
 
0.2%
ValueCountFrequency (%)
163.61
< 0.1%
157.81
< 0.1%
144.21
< 0.1%
1441
< 0.1%
139.61
< 0.1%
1371
< 0.1%
133.81
< 0.1%
131.41
< 0.1%
1311
< 0.1%
129.61
< 0.1%

kickoff_time
Categorical

HIGH CARDINALITY

Distinct1384
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2021-05-23T15:00:00Z
 
713
2017-05-21T14:00:00Z
 
683
2020-07-26T15:00:00Z
 
666
2018-05-13T14:00:00Z
 
647
2019-05-12T14:00:00Z
 
624
Other values (1379)
130265 

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters2671960
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016-08-15T19:00:00Z
2nd row2016-08-13T14:00:00Z
3rd row2016-08-14T15:00:00Z
4th row2016-08-13T14:00:00Z
5th row2016-08-15T19:00:00Z

Common Values

ValueCountFrequency (%)
2021-05-23T15:00:00Z713
 
0.5%
2017-05-21T14:00:00Z683
 
0.5%
2020-07-26T15:00:00Z666
 
0.5%
2018-05-13T14:00:00Z647
 
0.5%
2019-05-12T14:00:00Z624
 
0.5%
2022-02-19T15:00:00Z442
 
0.3%
2017-04-01T14:00:00Z406
 
0.3%
2017-02-04T15:00:00Z405
 
0.3%
2017-01-14T15:00:00Z390
 
0.3%
2017-01-31T19:45:00Z387
 
0.3%
Other values (1374)128235
96.0%

Length

2022-07-16T16:57:54.902962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-05-23t15:00:00z713
 
0.5%
2017-05-21t14:00:00z683
 
0.5%
2020-07-26t15:00:00z666
 
0.5%
2018-05-13t14:00:00z647
 
0.5%
2019-05-12t14:00:00z624
 
0.5%
2022-02-19t15:00:00z442
 
0.3%
2017-04-01t14:00:00z406
 
0.3%
2017-02-04t15:00:00z405
 
0.3%
2017-01-14t15:00:00z390
 
0.3%
2017-01-31t19:45:00z387
 
0.3%
Other values (1374)128235
96.0%

Most occurring characters

ValueCountFrequency (%)
0784718
29.4%
1376588
14.1%
2312031
 
11.7%
-267196
 
10.0%
:267196
 
10.0%
T133598
 
5.0%
Z133598
 
5.0%
372215
 
2.7%
568636
 
2.6%
962407
 
2.3%
Other values (4)193777
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1870372
70.0%
Dash Punctuation267196
 
10.0%
Other Punctuation267196
 
10.0%
Uppercase Letter267196
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0784718
42.0%
1376588
20.1%
2312031
 
16.7%
372215
 
3.9%
568636
 
3.7%
962407
 
3.3%
460157
 
3.2%
749060
 
2.6%
846735
 
2.5%
637825
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
T133598
50.0%
Z133598
50.0%
Dash Punctuation
ValueCountFrequency (%)
-267196
100.0%
Other Punctuation
ValueCountFrequency (%)
:267196
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2404764
90.0%
Latin267196
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0784718
32.6%
1376588
15.7%
2312031
 
13.0%
-267196
 
11.1%
:267196
 
11.1%
372215
 
3.0%
568636
 
2.9%
962407
 
2.6%
460157
 
2.5%
749060
 
2.0%
Other values (2)84560
 
3.5%
Latin
ValueCountFrequency (%)
T133598
50.0%
Z133598
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2671960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0784718
29.4%
1376588
14.1%
2312031
 
11.7%
-267196
 
10.0%
:267196
 
10.0%
T133598
 
5.0%
Z133598
 
5.0%
372215
 
2.7%
568636
 
2.6%
962407
 
2.3%
Other values (4)193777
 
7.3%

minutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct91
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.31013189
Minimum0
Maximum90
Zeros73252
Zeros (%)54.8%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-07-16T16:57:54.986465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q390
95-th percentile90
Maximum90
Range90
Interquartile range (IQR)90

Descriptive statistics

Standard deviation40.62748335
Coefficient of variation (CV)1.257422393
Kurtosis-1.562113344
Mean32.31013189
Median Absolute Deviation (MAD)0
Skewness0.5841632447
Sum4316569
Variance1650.592403
MonotonicityNot monotonic
2022-07-16T16:57:55.089964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
073252
54.8%
9036349
27.2%
451561
 
1.2%
1952
 
0.7%
77422
 
0.3%
12419
 
0.3%
10398
 
0.3%
79392
 
0.3%
78387
 
0.3%
11384
 
0.3%
Other values (81)19082
 
14.3%
ValueCountFrequency (%)
073252
54.8%
1952
 
0.7%
2298
 
0.2%
3274
 
0.2%
4295
 
0.2%
5308
 
0.2%
6343
 
0.3%
7365
 
0.3%
8372
 
0.3%
9357
 
0.3%
ValueCountFrequency (%)
9036349
27.2%
89194
 
0.1%
88248
 
0.2%
87295
 
0.2%
86282
 
0.2%
85288
 
0.2%
84314
 
0.2%
83345
 
0.3%
82378
 
0.3%
81358
 
0.3%

opponent_team
Real number (ℝ≥0)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.50653453
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-07-16T16:57:55.170463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median11
Q316
95-th percentile20
Maximum20
Range19
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.771480137
Coefficient of variation (CV)0.5493229115
Kurtosis-1.207590133
Mean10.50653453
Median Absolute Deviation (MAD)5
Skewness-0.002217076822
Sum1403652
Variance33.30998297
MonotonicityNot monotonic
2022-07-16T16:57:55.240463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
206732
 
5.0%
36725
 
5.0%
106723
 
5.0%
116714
 
5.0%
146713
 
5.0%
46712
 
5.0%
26712
 
5.0%
176707
 
5.0%
196706
 
5.0%
156694
 
5.0%
Other values (10)66460
49.7%
ValueCountFrequency (%)
16673
5.0%
26712
5.0%
36725
5.0%
46712
5.0%
56609
4.9%
66632
5.0%
76682
5.0%
86593
4.9%
96619
5.0%
106723
5.0%
ValueCountFrequency (%)
206732
5.0%
196706
5.0%
186639
5.0%
176707
5.0%
166687
5.0%
156694
5.0%
146713
5.0%
136651
5.0%
126675
5.0%
116714
5.0%

own_goals
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
133400 
1
 
198

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters133598
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0133400
99.9%
1198
 
0.1%

Length

2022-07-16T16:57:55.317462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-16T16:57:55.385962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0133400
99.9%
1198
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0133400
99.9%
1198
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number133598
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0133400
99.9%
1198
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common133598
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0133400
99.9%
1198
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII133598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0133400
99.9%
1198
 
0.1%

penalties_missed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
133475 
1
 
123

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters133598
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0133475
99.9%
1123
 
0.1%

Length

2022-07-16T16:57:55.445963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-16T16:57:55.513965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0133475
99.9%
1123
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0133475
99.9%
1123
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number133598
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0133475
99.9%
1123
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common133598
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0133475
99.9%
1123
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII133598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0133475
99.9%
1123
 
0.1%

penalties_saved
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
133507 
1
 
89
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters133598
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0133507
99.9%
189
 
0.1%
22
 
< 0.1%

Length

2022-07-16T16:57:55.571963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-16T16:57:55.641964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0133507
99.9%
189
 
0.1%
22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0133507
99.9%
189
 
0.1%
22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number133598
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0133507
99.9%
189
 
0.1%
22
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common133598
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0133507
99.9%
189
 
0.1%
22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII133598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0133507
99.9%
189
 
0.1%
22
 
< 0.1%

red_cards
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
133345 
1
 
253

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters133598
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0133345
99.8%
1253
 
0.2%

Length

2022-07-16T16:57:55.702465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-16T16:57:55.770964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0133345
99.8%
1253
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0133345
99.8%
1253
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number133598
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0133345
99.8%
1253
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common133598
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0133345
99.8%
1253
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII133598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0133345
99.8%
1253
 
0.2%

round
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.15223282
Minimum1
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-07-16T16:57:55.837965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median20
Q329
95-th percentile38
Maximum47
Range46
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.47187289
Coefficient of variation (CV)0.5692606368
Kurtosis-0.9051103233
Mean20.15223282
Median Absolute Deviation (MAD)9
Skewness0.1590178753
Sum2692298
Variance131.6038675
MonotonicityNot monotonic
2022-07-16T16:57:55.922965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
264534
 
3.4%
253978
 
3.0%
243944
 
3.0%
223878
 
2.9%
233862
 
2.9%
273838
 
2.9%
293798
 
2.8%
193761
 
2.8%
283689
 
2.8%
213649
 
2.7%
Other values (37)94667
70.9%
ValueCountFrequency (%)
13065
2.3%
23214
2.4%
33270
2.4%
43355
2.5%
53395
2.5%
63416
2.6%
73438
2.6%
83459
2.6%
93473
2.6%
103491
2.6%
ValueCountFrequency (%)
47666
 
0.5%
46665
 
0.5%
45661
 
0.5%
44654
 
0.5%
43653
 
0.5%
42652
 
0.5%
41648
 
0.5%
40644
 
0.5%
39761
 
0.6%
382667
2.0%

saves
Real number (ℝ≥0)

ZEROS

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0958247878
Minimum0
Maximum14
Zeros129543
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-07-16T16:57:55.995961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6323516765
Coefficient of variation (CV)6.599040718
Kurtosis77.214323
Mean0.0958247878
Median Absolute Deviation (MAD)0
Skewness8.138692724
Sum12802
Variance0.3998686428
MonotonicityNot monotonic
2022-07-16T16:57:56.057963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0129543
97.0%
2961
 
0.7%
1816
 
0.6%
3808
 
0.6%
4612
 
0.5%
5383
 
0.3%
6244
 
0.2%
7125
 
0.1%
852
 
< 0.1%
934
 
< 0.1%
Other values (5)20
 
< 0.1%
ValueCountFrequency (%)
0129543
97.0%
1816
 
0.6%
2961
 
0.7%
3808
 
0.6%
4612
 
0.5%
5383
 
0.3%
6244
 
0.2%
7125
 
0.1%
852
 
< 0.1%
934
 
< 0.1%
ValueCountFrequency (%)
141
 
< 0.1%
131
 
< 0.1%
122
 
< 0.1%
115
 
< 0.1%
1011
 
< 0.1%
934
 
< 0.1%
852
 
< 0.1%
7125
 
0.1%
6244
0.2%
5383
0.3%

selected
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct77275
Distinct (%)57.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159575.9188
Minimum0
Maximum6483921
Zeros740
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-07-16T16:57:56.142464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile506
Q15355
median23682.5
Q3121308.25
95-th percentile804247.55
Maximum6483921
Range6483921
Interquartile range (IQR)115953.25

Descriptive statistics

Standard deviation390345.3434
Coefficient of variation (CV)2.446141914
Kurtosis37.77205216
Mean159575.9188
Median Absolute Deviation (MAD)22319.5
Skewness5.222675061
Sum2.13190236 × 1010
Variance1.523694871 × 1011
MonotonicityNot monotonic
2022-07-16T16:57:56.233464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0740
 
0.6%
3940
 
< 0.1%
28926
 
< 0.1%
6025
 
< 0.1%
4025
 
< 0.1%
32124
 
< 0.1%
45923
 
< 0.1%
32323
 
< 0.1%
27322
 
< 0.1%
32922
 
< 0.1%
Other values (77265)132628
99.3%
ValueCountFrequency (%)
0740
0.6%
41
 
< 0.1%
61
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
102
 
< 0.1%
112
 
< 0.1%
122
 
< 0.1%
134
 
< 0.1%
143
 
< 0.1%
ValueCountFrequency (%)
64839211
< 0.1%
64670291
< 0.1%
64140191
< 0.1%
63758741
< 0.1%
63100031
< 0.1%
62946281
< 0.1%
61992281
< 0.1%
61166081
< 0.1%
60234501
< 0.1%
58479881
< 0.1%

team_a_score
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)< 0.1%
Missing59
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.236799736
Minimum0
Maximum9
Zeros43513
Zeros (%)32.6%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-07-16T16:57:56.311466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.210256602
Coefficient of variation (CV)0.9785388586
Kurtosis1.560390217
Mean1.236799736
Median Absolute Deviation (MAD)1
Skewness1.098277108
Sum165161
Variance1.464721044
MonotonicityNot monotonic
2022-07-16T16:57:56.370965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
143773
32.8%
043513
32.6%
226728
20.0%
312877
 
9.6%
44730
 
3.5%
51432
 
1.1%
6297
 
0.2%
7131
 
0.1%
958
 
< 0.1%
(Missing)59
 
< 0.1%
ValueCountFrequency (%)
043513
32.6%
143773
32.8%
226728
20.0%
312877
 
9.6%
44730
 
3.5%
51432
 
1.1%
6297
 
0.2%
7131
 
0.1%
958
 
< 0.1%
ValueCountFrequency (%)
958
 
< 0.1%
7131
 
0.1%
6297
 
0.2%
51432
 
1.1%
44730
 
3.5%
312877
 
9.6%
226728
20.0%
143773
32.8%
043513
32.6%

team_h_score
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing59
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.506608556
Minimum0
Maximum9
Zeros32545
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-07-16T16:57:56.432963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.309143275
Coefficient of variation (CV)0.8689339178
Kurtosis1.289726505
Mean1.506608556
Median Absolute Deviation (MAD)1
Skewness0.9900795368
Sum201191
Variance1.713856115
MonotonicityNot monotonic
2022-07-16T16:57:56.491964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
143028
32.2%
032545
24.4%
231285
23.4%
316264
 
12.2%
46743
 
5.0%
52772
 
2.1%
6541
 
0.4%
7237
 
0.2%
972
 
0.1%
852
 
< 0.1%
(Missing)59
 
< 0.1%
ValueCountFrequency (%)
032545
24.4%
143028
32.2%
231285
23.4%
316264
 
12.2%
46743
 
5.0%
52772
 
2.1%
6541
 
0.4%
7237
 
0.2%
852
 
< 0.1%
972
 
0.1%
ValueCountFrequency (%)
972
 
0.1%
852
 
< 0.1%
7237
 
0.2%
6541
 
0.4%
52772
 
2.1%
46743
 
5.0%
316264
 
12.2%
231285
23.4%
143028
32.2%
032545
24.4%

threat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct153
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.370432192
Minimum0
Maximum199
Zeros94571
Zeros (%)70.8%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-07-16T16:57:56.572464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile34
Maximum199
Range199
Interquartile range (IQR)2

Descriptive statistics

Standard deviation13.58207808
Coefficient of variation (CV)2.529047495
Kurtosis18.21792722
Mean5.370432192
Median Absolute Deviation (MAD)0
Skewness3.77329199
Sum717479
Variance184.4728451
MonotonicityNot monotonic
2022-07-16T16:57:56.665963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
094571
70.8%
26066
 
4.5%
43723
 
2.8%
82171
 
1.6%
61913
 
1.4%
11340
 
1.0%
171229
 
0.9%
101147
 
0.9%
71090
 
0.8%
18964
 
0.7%
Other values (143)19384
 
14.5%
ValueCountFrequency (%)
094571
70.8%
11340
 
1.0%
26066
 
4.5%
3672
 
0.5%
43723
 
2.8%
5458
 
0.3%
61913
 
1.4%
71090
 
0.8%
82171
 
1.6%
9871
 
0.7%
ValueCountFrequency (%)
1991
< 0.1%
1861
< 0.1%
1811
< 0.1%
1761
< 0.1%
1612
< 0.1%
1541
< 0.1%
1522
< 0.1%
1512
< 0.1%
1481
< 0.1%
1471
< 0.1%

total_points
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.346277639
Minimum-7
Maximum29
Zeros76033
Zeros (%)56.9%
Negative606
Negative (%)0.5%
Memory size1.0 MiB
2022-07-16T16:57:57.222964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-7
5-th percentile0
Q10
median0
Q32
95-th percentile7
Maximum29
Range36
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.502680141
Coefficient of variation (CV)1.858962868
Kurtosis9.382405114
Mean1.346277639
Median Absolute Deviation (MAD)0
Skewness2.781693113
Sum179860
Variance6.26340789
MonotonicityNot monotonic
2022-07-16T16:57:57.301963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
076033
56.9%
119755
 
14.8%
217298
 
12.9%
34672
 
3.5%
64635
 
3.5%
52212
 
1.7%
71903
 
1.4%
81660
 
1.2%
91187
 
0.9%
41057
 
0.8%
Other values (22)3186
 
2.4%
ValueCountFrequency (%)
-71
 
< 0.1%
-61
 
< 0.1%
-46
 
< 0.1%
-337
 
< 0.1%
-2159
 
0.1%
-1402
 
0.3%
076033
56.9%
119755
 
14.8%
217298
 
12.9%
34672
 
3.5%
ValueCountFrequency (%)
291
 
< 0.1%
261
 
< 0.1%
246
 
< 0.1%
232
 
< 0.1%
2120
 
< 0.1%
2018
 
< 0.1%
1921
 
< 0.1%
1835
 
< 0.1%
1748
< 0.1%
16100
0.1%

transfers_balance
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct35511
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean893.1517762
Minimum-1857821
Maximum1983733
Zeros4824
Zeros (%)3.6%
Negative88363
Negative (%)66.1%
Memory size1.0 MiB
2022-07-16T16:57:57.395462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1857821
5-th percentile-24104
Q1-1235
median-69
Q344
95-th percentile27738.5
Maximum1983733
Range3841554
Interquartile range (IQR)1279

Descriptive statistics

Standard deviation51237.70989
Coefficient of variation (CV)57.36730448
Kurtosis188.491352
Mean893.1517762
Median Absolute Deviation (MAD)554
Skewness0.6050967415
Sum119323291
Variance2625302915
MonotonicityNot monotonic
2022-07-16T16:57:57.488963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04824
 
3.6%
-1976
 
0.7%
-2954
 
0.7%
-3859
 
0.6%
-4786
 
0.6%
-5714
 
0.5%
-7670
 
0.5%
-6645
 
0.5%
-8604
 
0.5%
1596
 
0.4%
Other values (35501)121970
91.3%
ValueCountFrequency (%)
-18578211
< 0.1%
-17342841
< 0.1%
-14574401
< 0.1%
-13475611
< 0.1%
-13095661
< 0.1%
-13080541
< 0.1%
-12998961
< 0.1%
-12859031
< 0.1%
-12457781
< 0.1%
-12453931
< 0.1%
ValueCountFrequency (%)
19837331
< 0.1%
19072291
< 0.1%
12884941
< 0.1%
12560752
< 0.1%
12538521
< 0.1%
10908321
< 0.1%
10892982
< 0.1%
10333581
< 0.1%
10118951
< 0.1%
9959751
< 0.1%

transfers_in
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct26237
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11105.49463
Minimum0
Maximum2104464
Zeros9059
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-07-16T16:57:57.590964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q142
median349
Q33762
95-th percentile54316.7
Maximum2104464
Range2104464
Interquartile range (IQR)3720

Descriptive statistics

Standard deviation44234.15593
Coefficient of variation (CV)3.983087417
Kurtosis201.376068
Mean11105.49463
Median Absolute Deviation (MAD)347
Skewness10.71981616
Sum1483671872
Variance1956660551
MonotonicityNot monotonic
2022-07-16T16:57:57.680464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09059
 
6.8%
21014
 
0.8%
1993
 
0.7%
3930
 
0.7%
4903
 
0.7%
5899
 
0.7%
6820
 
0.6%
7813
 
0.6%
8765
 
0.6%
10761
 
0.6%
Other values (26227)116641
87.3%
ValueCountFrequency (%)
09059
6.8%
1993
 
0.7%
21014
 
0.8%
3930
 
0.7%
4903
 
0.7%
5899
 
0.7%
6820
 
0.6%
7813
 
0.6%
8765
 
0.6%
9748
 
0.6%
ValueCountFrequency (%)
21044641
< 0.1%
19917311
< 0.1%
13187511
< 0.1%
12894561
< 0.1%
12639242
< 0.1%
11935871
< 0.1%
11278671
< 0.1%
11143432
< 0.1%
10645591
< 0.1%
10555011
< 0.1%

transfers_out
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct28652
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10211.8597
Minimum0
Maximum1872898
Zeros4217
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-07-16T16:57:57.772962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q1125
median927
Q36087
95-th percentile46008.95
Maximum1872898
Range1872898
Interquartile range (IQR)5962

Descriptive statistics

Standard deviation37826.58606
Coefficient of variation (CV)3.704181919
Kurtosis373.4981141
Mean10211.8597
Median Absolute Deviation (MAD)908
Skewness14.59712497
Sum1364284032
Variance1430850613
MonotonicityNot monotonic
2022-07-16T16:57:57.865462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04217
 
3.2%
4557
 
0.4%
2525
 
0.4%
10490
 
0.4%
9482
 
0.4%
8481
 
0.4%
7480
 
0.4%
1480
 
0.4%
3466
 
0.3%
5458
 
0.3%
Other values (28642)124962
93.5%
ValueCountFrequency (%)
04217
3.2%
1480
 
0.4%
2525
 
0.4%
3466
 
0.3%
4557
 
0.4%
5458
 
0.3%
6439
 
0.3%
7480
 
0.4%
8481
 
0.4%
9482
 
0.4%
ValueCountFrequency (%)
18728981
< 0.1%
17826621
< 0.1%
15712131
< 0.1%
13954001
< 0.1%
13910351
< 0.1%
13820441
< 0.1%
13801651
< 0.1%
13436101
< 0.1%
12911381
< 0.1%
12880892
< 0.1%

value
Real number (ℝ≥0)

HIGH CORRELATION

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.59444752
Minimum37
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-07-16T16:57:57.961463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile40
Q144
median48
Q355
95-th percentile75
Maximum136
Range99
Interquartile range (IQR)11

Descriptive statistics

Standard deviation12.48928334
Coefficient of variation (CV)0.2420664227
Kurtosis9.05505825
Mean51.59444752
Median Absolute Deviation (MAD)5
Skewness2.617414974
Sum6892915
Variance155.9821984
MonotonicityNot monotonic
2022-07-16T16:57:58.052464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4518635
 
13.9%
4411277
 
8.4%
509157
 
6.9%
438703
 
6.5%
408701
 
6.5%
496660
 
5.0%
486182
 
4.6%
554996
 
3.7%
544888
 
3.7%
474824
 
3.6%
Other values (90)49575
37.1%
ValueCountFrequency (%)
3711
 
< 0.1%
38253
 
0.2%
392329
 
1.7%
408701
6.5%
41983
 
0.7%
423985
 
3.0%
438703
6.5%
4411277
8.4%
4518635
13.9%
462863
 
2.1%
ValueCountFrequency (%)
1365
 
< 0.1%
1352
 
< 0.1%
1342
 
< 0.1%
1335
 
< 0.1%
13210
 
< 0.1%
13115
< 0.1%
13027
< 0.1%
12920
< 0.1%
12836
< 0.1%
12722
< 0.1%

was_home
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size130.6 KiB
False
66818 
True
66780 
ValueCountFrequency (%)
False66818
50.0%
True66780
50.0%
2022-07-16T16:57:58.135464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

yellow_cards
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
126436 
1
 
7162

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters133598
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0126436
94.6%
17162
 
5.4%

Length

2022-07-16T16:57:58.197964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-16T16:57:58.265461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0126436
94.6%
17162
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0126436
94.6%
17162
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number133598
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0126436
94.6%
17162
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common133598
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0126436
94.6%
17162
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII133598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0126436
94.6%
17162
 
5.4%

GW
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.15223282
Minimum1
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-07-16T16:57:58.331964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median20
Q329
95-th percentile38
Maximum47
Range46
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.47187289
Coefficient of variation (CV)0.5692606368
Kurtosis-0.9051103233
Mean20.15223282
Median Absolute Deviation (MAD)9
Skewness0.1590178753
Sum2692298
Variance131.6038675
MonotonicityNot monotonic
2022-07-16T16:57:58.417463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
264534
 
3.4%
253978
 
3.0%
243944
 
3.0%
223878
 
2.9%
233862
 
2.9%
273838
 
2.9%
293798
 
2.8%
193761
 
2.8%
283689
 
2.8%
213649
 
2.7%
Other values (37)94667
70.9%
ValueCountFrequency (%)
13065
2.3%
23214
2.4%
33270
2.4%
43355
2.5%
53395
2.5%
63416
2.6%
73438
2.6%
83459
2.6%
93473
2.6%
103491
2.6%
ValueCountFrequency (%)
47666
 
0.5%
46665
 
0.5%
45661
 
0.5%
44654
 
0.5%
43653
 
0.5%
42652
 
0.5%
41648
 
0.5%
40644
 
0.5%
39761
 
0.6%
382667
2.0%

position
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
MID
55126 
DEF
45353 
FWD
18615 
GK
14504 

Length

Max length3
Median length3
Mean length2.891435501
Min length2

Characters and Unicode

Total characters386290
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEF
2nd rowMID
3rd rowMID
4th rowMID
5th rowDEF

Common Values

ValueCountFrequency (%)
MID55126
41.3%
DEF45353
33.9%
FWD18615
 
13.9%
GK14504
 
10.9%

Length

2022-07-16T16:57:58.495964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-16T16:57:58.570461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
mid55126
41.3%
def45353
33.9%
fwd18615
 
13.9%
gk14504
 
10.9%

Most occurring characters

ValueCountFrequency (%)
D119094
30.8%
F63968
16.6%
M55126
14.3%
I55126
14.3%
E45353
 
11.7%
W18615
 
4.8%
G14504
 
3.8%
K14504
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter386290
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D119094
30.8%
F63968
16.6%
M55126
14.3%
I55126
14.3%
E45353
 
11.7%
W18615
 
4.8%
G14504
 
3.8%
K14504
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin386290
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D119094
30.8%
F63968
16.6%
M55126
14.3%
I55126
14.3%
E45353
 
11.7%
W18615
 
4.8%
G14504
 
3.8%
K14504
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII386290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D119094
30.8%
F63968
16.6%
M55126
14.3%
I55126
14.3%
E45353
 
11.7%
W18615
 
4.8%
G14504
 
3.8%
K14504
 
3.8%

season
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2020-21
24365 
2016-17
23679 
2019-20
22560 
2017-18
22467 
2018-19
21790 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters935186
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016-17
2nd row2016-17
3rd row2016-17
4th row2016-17
5th row2016-17

Common Values

ValueCountFrequency (%)
2020-2124365
18.2%
2016-1723679
17.7%
2019-2022560
16.9%
2017-1822467
16.8%
2018-1921790
16.3%
2021-2218737
14.0%

Length

2022-07-16T16:57:58.640964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-16T16:57:58.719961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2020-2124365
18.2%
2016-1723679
17.7%
2019-2022560
16.9%
2017-1822467
16.8%
2018-1921790
16.3%
2021-2218737
14.0%

Most occurring characters

ValueCountFrequency (%)
2261099
27.9%
1201534
21.6%
0180523
19.3%
-133598
14.3%
746146
 
4.9%
944350
 
4.7%
844257
 
4.7%
623679
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number801588
85.7%
Dash Punctuation133598
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2261099
32.6%
1201534
25.1%
0180523
22.5%
746146
 
5.8%
944350
 
5.5%
844257
 
5.5%
623679
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
-133598
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common935186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2261099
27.9%
1201534
21.6%
0180523
19.3%
-133598
14.3%
746146
 
4.9%
944350
 
4.7%
844257
 
4.7%
623679
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII935186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2261099
27.9%
1201534
21.6%
0180523
19.3%
-133598
14.3%
746146
 
4.9%
944350
 
4.7%
844257
 
4.7%
623679
 
2.5%

Interactions

2022-07-16T16:57:48.683967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:56:57.859964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:00.444467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:03.008467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:05.664967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:08.175463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:10.634969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:13.061967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:15.598464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:18.081968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:20.672966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:23.078465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:25.651464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:28.167968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:30.582962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:33.063966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:35.760963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:38.277966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:40.896964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:43.312468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:46.225465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:48.797967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:56:57.987968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:00.559467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:03.131962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:05.785467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:08.288470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:10.751967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:13.179962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:15.707461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:18.203967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:20.793969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:23.198470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:25.770463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:28.283966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:30.700963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:33.182966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:35.882462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:38.406468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:41.013466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:43.432962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:46.343967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:48.911962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:56:58.115971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:00.675471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:03.256464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:05.906464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:08.401968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:10.868466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:13.298962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:15.820964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:18.326962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:20.911964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:23.333462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:25.889968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:28.401962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:30.817964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:33.299468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:36.004967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:38.537970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:41.129967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:43.555473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:46.463462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:49.026468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:56:58.242463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:00.792966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:03.378464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:06.026469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:08.519965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:10.985467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:13.417467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:15.931968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:18.450963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-07-16T16:57:50.149471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:56:59.477964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:02.037469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:04.588962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:07.216467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:09.620462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:12.137963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:14.607467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:17.195468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:19.668463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:22.158462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-07-16T16:57:27.222462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:29.664969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:32.104967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:34.832964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:37.317466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-07-16T16:57:42.391967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:44.901968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:47.747968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:50.263968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:56:59.592467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:02.156466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:04.705464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:07.329966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:09.724968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:12.249468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:14.727965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:17.299967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-07-16T16:57:24.864462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-07-16T16:57:02.279470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-16T16:57:04.829462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-07-16T16:57:45.146464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-07-16T16:57:07.573463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-07-16T16:57:02.775461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-07-16T16:57:48.573962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-07-16T16:57:58.822464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-16T16:57:59.030464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-16T16:57:59.239465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-16T16:57:59.424464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-07-16T16:57:59.556964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-16T16:57:51.188463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-16T16:57:52.012463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-07-16T16:57:52.537465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-07-16T16:57:52.719969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

nameassistsbonusbpsclean_sheetscreativityelementfixturegoals_concededgoals_scoredict_indexinfluencekickoff_timeminutesopponent_teamown_goalspenalties_missedpenalties_savedred_cardsroundsavesselectedteam_a_scoreteam_h_scorethreattotal_pointstransfers_balancetransfers_intransfers_outvaluewas_homeyellow_cardsGWpositionseason
0Aaron_Cresswell00000.045410000.00.02016-08-15T19:00:00Z04000010140231.02.00.0000055False01DEF2016-17
1Aaron_Lennon00600.31423000.98.22016-08-13T14:00:00Z1517000010139181.01.00.0100060True01MID2016-17
2Aaron_Ramsey00504.9168303.02.22016-08-14T15:00:00Z6090000101631704.03.023.0200080True01MID2016-17
3Abdoulaye_Doucouré00000.04827000.00.02016-08-13T14:00:00Z01300001010511.01.00.0000050False01MID2016-17
4Abdul Rahman_Baba00000.08010000.00.02016-08-15T19:00:00Z02000001012431.02.00.0000055True01DEF2016-17
5Abel_Hernández1010012.21634105.714.42016-08-13T11:30:00Z908000010260391.02.030.0500060True01FWD2016-17
6Adama_Diomande0229016.816441110.745.22016-08-13T11:30:00Z908000010381511.02.045.0800045True01FWD2016-17
7Adam_Clayton00602.22836101.43.22016-08-13T14:00:00Z9014000010176631.01.09.0200045True01MID2016-17
8Adam_Federici00000.0309000.00.02016-08-14T12:30:00Z01100001043153.01.00.0000045True01GK2016-17
9Adam_Forshaw00301.32866100.32.02016-08-13T14:00:00Z691400001027231.01.00.0100045True11MID2016-17

Last rows

nameassistsbonusbpsclean_sheetscreativityelementfixturegoals_concededgoals_scoredict_indexinfluencekickoff_timeminutesopponent_teamown_goalspenalties_missedpenalties_savedred_cardsroundsavesselectedteam_a_scoreteam_h_scorethreattotal_pointstransfers_balancetransfers_intransfers_outvaluewas_homeyellow_cardsGWpositionseason
133588Kasper Schmeichel002000.0200293104.140.62022-03-20T14:00:00Z903000030514062921.02.00.035820965530732148True030GK2021-22
133589Ben White012610.467291002.019.22022-03-19T12:30:00Z902000030012380951.00.00.0742553526771012445False030DEF2021-22
133590Kaine Hayden00000.0684291000.00.02022-03-19T12:30:00Z01000030011911.00.00.00471096240True030DEF2021-22
133591Daniel Castelo Podence1024034.6438300208.834.02022-03-18T20:00:00Z771000003001029203.02.019.052223224888265655True030MID2021-22
133592Halil Dervişoğlu00000.095293000.00.02022-03-20T14:00:00Z09000030010731.02.00.00-30354False030FWD2021-22
133593Kurt Zouma00601.8128298300.20.62022-03-20T16:30:00Z901710003001467571.03.00.0-11079612551175554False030DEF2021-22
133594Aaron Cresswell0015014.8411298304.122.02022-03-20T16:30:00Z901700003008243871.03.04.0118750355701682054False030DEF2021-22
133595John Ruddy00000.0452300000.00.02022-03-18T20:00:00Z0100000300168053.02.00.006317010743True030GK2021-22
133596Wilfred Ndidi00000.0216293000.00.02022-03-20T14:00:00Z030000300753931.02.00.00-20753874548True030MID2021-22
133597Ryan Fredericks00000.0415298000.00.02022-03-20T16:30:00Z017000030086861.03.00.00811395844False030DEF2021-22